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The study attempts to analyze the learning performance of neural
networks in applications, and propose a new learning procedure
for the layered feedforward neural network systems, named RNBP,
which binds RN and BP learning algorithms. Two artificial neural
networks, BP and RNBP, here are both applied to two financial
fields, the simulation of Black-Scholes pricing model for the
call options and the midrates forecasting in financial swaps.
The explicit performance comparison between the two artificial
neuralThen we propound a mathematical methodology of sensitivity
analysis and the dead regions to deeply explore inside the
network structures to see whether the models of ANNS are
actually well trained or valid, and thus setup an alternative
comparable criterion. The results from this study show that RNBP
performs better than BP in forecasting effectiveness, but RNBP
obtains neither a consistent learning efficiency in cases nor a
stable forecasting ability. Furthermore, the sensitivity
analysis and the dead regiIn the past, most studies applying
neural networks ignored the importance that it is feasible and
advantageous to obtain more useful information via analyzing
neural networks. The purpose of the research is to help further
understanding to the information discovery resulted from neural
networks in practical applicati
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